Machine Learning I
Course Number: 46926
Concentration: Statistics / Data Science
Semester(s): Mini 2
Required/Elective: Required
Prerequisite(s): 46923
The first in a two-part sequence covering statistical machine learning aimed at quantitative finance. This first course covers tools and approaches for prediction, including both regression and classification. The focus is on understanding the foundations of the methods so that they can be both applied and modified. Topics include foundations of supervised learning, bias-variance tradeoffs, model validation and assessment, classification and classification metrics, regularized and nonparametric regression, generalized additive models, support vector machines, and tree-based methods.